In this section, we aim to improve the data efficiency of existing detection transformers, while making minimum modifications to their original designs. Firstly, we provide a brief revisiting of the existing detection transformers. Subsequently, based on the experiments and analysis in the previous section, we make minor modifications to the existing data-hungry detection transformer models, like DETR [1]} and CondDETR [2]}, to significantly improve their data efficiency. Finally, we propose a simple yet effective label augmentation method to provide richer supervised signals to detection transformers to further improve their data efficiency.
